Distribution analysis of 0.025X filtered data
Tim Read
August 13, 2015
Starting form the filtered table from ‘HMP_coverage.Rmd’. Run a series of analysis to look at relationships between body site and subjects.
print(date())## [1] "Wed Oct 21 16:24:00 2015"library(reshape2)
#library(igraph)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#library(biomod2)
library(e1071)
library(RColorBrewer)
library(gdata)## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.sizelibrary(vegan)## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.3-0library(assertthat)
source('./staph_metagenome_tools.R', echo=TRUE)##
## > bintr <- function(mat, cutoff) {
## + mat[which(mat > cutoff)] <- 1
## + mat[which(!(mat > cutoff))] <- 0
## + return(mat)
## + }
##
## > calc_FTS <- function(pop, mini) {
## + fishmat <- matrix(c(mini[1], mini[2], pop[1] - mini[1], pop[2] -
## + mini[2]), ncol = 2, nrow = 2)
## + .... [TRUNCATED]
##
## > calc_hits <- function(nameset, mat) {
## + minimat <- select(mat, one_of(nameset))[rownames(mat) %in%
## + nameset, ]
## + minimat.size <- ( .... [TRUNCATED]
##
## > calc_hits_slice <- function(nameset, mat) {
## + minimat <- slice(mat, nameset)[, nameset]
## + minimat.hits <- sum(minimat)/2
## + return(minima .... [TRUNCATED]
##
## > create_cooccur_mat <- function(mat) {
## + library(reshape2)
## + dat2 <- melt(mat)
## + w <- dcast(dat2, V2 ~ V1)
## + x <- as.matrix(w[, -1])
## .... [TRUNCATED]
##
## > genotypes_plot <- function(mat, tit) {
## + top_genos <- c("CC_30", "CC_8", "CC_45", "CC_398", "CC_133",
## + "CC_59", "CC_15", "CC_97", "CC_ ..." ... [TRUNCATED]
##
## > all_genotypes_plot <- function(mat, tit) {
## + cS <- colSums(mat)
## + barplot(cS, main = tit, las = 3, cex.names = 0.8, col = "gray")
## + }
##
## > run_bs_subj_adonis <- function(df, bs_vec, subj_vec) {
## + library(e1071)
## + library(vegan)
## + body_site_adonis <- adonis(df ~ bs_vec)
## + .... [TRUNCATED]
##
## > make_subtype_matrix <- function(df) {
## + library(dplyr)
## + mat <- select(df, matches("CC")) %>% as.matrix
## + assert_that(dim(mat)[2] == 33) .... [TRUNCATED]
##
## > plot_coverages <- function(combined.df, titl) {
## + check_staph_df(combined.df)
## + par(mar = c(12, 4, 4, 2), cex = 0.8)
## + with(combined.df, .... [TRUNCATED]
##
## > plot_adjusted_coverages <- function(combined.df, titl) {
## + check_staph_df(combined.df)
## + stcols <- grep("CC|MLST", colnames(combined.df))
## + .... [TRUNCATED]
##
## > plot_mecA <- function(combined.df, titl) {
## + check_staph_df(combined.df)
## + with(combined.df, plot(Staph_cov, mecA_cov, col = Body.site,
## + .... [TRUNCATED]
##
## > plot_diversity_vers_cov <- function(combined.df, titl) {
## + library(vegan)
## + check_staph_df(combined.df)
## + stcols <- grep("CC|MLST", coln .... [TRUNCATED]
##
## > check_staph_df <- function(df) {
## + library(assertthat)
## + assert_that(length(grep("Body.site", colnames(df))) == 1)
## + assert_that(length( .... [TRUNCATED]
##
## > subject_perm <- function(df, multiSubjects, hamming_mat) {
## + library(gdata)
## + check_staph_df(df)
## + sub1.hits = 0
## + sub1.cells = 0
## + .... [TRUNCATED]
##
## > by_factor_perm <- function(bs, df, hamming_mat) {
## + check_staph_df(df)
## + for (i in bs) {
## + bss_rows <- which(df$Body.site == i)
## + .... [TRUNCATED]
##
## > intra_body_FTS <- function(body1, body2, df, multiSubjects,
## + u) {
## + library(dplyr)
## + check_staph_df(df)
## + temp.an <- filter(df, Bo .... [TRUNCATED]
##
## > merge_CCs <- function(in_data, CC) {
## + new_col <- select(in_data, matches(CC)) %>% rowSums()
## + in_data <- select(in_data, -(matches(CC)))
## + .... [TRUNCATED]
##
## > plot_CC_types <- function(CC, CCcol = "red", mat,
## + SRA_file, map11, map10, plotdir, cutoff = 0.2) {
## + library(RgoogleMaps)
## + crows <- .... [TRUNCATED]
##
## > avg_geog_dist <- function(p) {
## + mat <- as.data.frame(combinations(nrow(p), 2))
## + dist_vec <- sapply(1:nrow(mat), function(x) distance.chord .... [TRUNCATED]
##
## > rand_distances <- function(n, latlon, perms = 1000) {
## + res_vec <- replicate(perms, avg_geog_dist(sample_n(latlon,
## + n)), simplify = "v ..." ... [TRUNCATED]
##
## > distance.chord <- function(point1, point2) {
## + R <- 6371
## + p1rad <- point1 * pi/180
## + p2rad <- point2 * pi/180
## + lat <- p1rad[2]
## + .... [TRUNCATED]
##
## > CC_geog_perm_test <- function(SRA_file, CC, cutoff,
## + s = 234523, reps = 1000) {
## + crows <- which(SRA_file[[CC]] > cutoff)
## + CC_df <- s .... [TRUNCATED]
##
## > decorate_staph_tree <- function(CC, tree, strains,
## + cutoff = 0.65, deco = "red") {
## + tag_list <- filter(strains, grepl(CC, Reference.CC)) .... [TRUNCATED]Read in data file created in earlier pipeline
dat4 <- read.table("./Data/cov0.025")Create data files
#list of all subjects with more than one sample
multiSubjects <- count(dat4,Subject.Id) %>% filter(n > 1) %>% select(Subject.Id )
dat5 <- make_subtype_matrix(dat4)
#create Hamming dist matrices with and without cutof min value of 0.2
dat4$Subject.Id <- as.factor(dat4$Subject.Id)
dat6 <- make_subtype_matrix(dat4) %>% bintr(0.2) %>% hamming.distance %>% data.frame
dat8 <- make_subtype_matrix(dat4) %>% hamming.distance %>% data.frame PERMANOVA
test for significant associations of subtype with with bodysite and subject. us e Hamming dist. matrix. Two levels, one with a beta cutoff for all samples > 0.2 and one without
set.seed(344098)
run_bs_subj_adonis(dat6,dat4$Body.site,dat4$Subject.Id)##
## Call:
## adonis(formula = df ~ bs_vec)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## bs_vec 13 1.0182 0.078325 3.3511 0.12427 0.001 ***
## Residuals 307 7.1755 0.023373 0.87573
## Total 320 8.1937 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist(df), group = bs_vec)
##
## No. of Positive Eigenvalues: 31
## No. of Negative Eigenvalues: 0
##
## Average distance to median:
## anterior nares attached keratinized gingiva
## 14.787 10.909
## buccal mucosa hard palate
## 13.726 0.000
## left retroauricular crease palatine tonsil
## 15.135 12.773
## posterior fornnix right antecubital fossa
## 9.198 0.000
## right retroauricular crease saliva
## 18.199 0.000
## stool subgingival_plaque
## 10.353 0.000
## supragingival plaque tongue dorsum
## 13.614 16.227
##
## Eigenvalues for PCoA axes:
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7
## 38212.716 28710.788 7635.598 3541.055 2363.256 1893.255 1344.076
## PCoA8
## 1007.922
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 13 1981.505 152.42350 4.794898 999 0.001
## Residuals 307 9759.126 31.78868 NA NA NA
##
## Call:
## adonis(formula = df ~ subj_vec)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## subj_vec 107 3.2944 0.030789 1.3385 0.40206 0.004 **
## Residuals 213 4.8993 0.023002 0.59794
## Total 320 8.1937 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist(df), group = subj_vec)
##
## No. of Positive Eigenvalues: 31
## No. of Negative Eigenvalues: 0
##
## Average distance to median:
## 1 2 3 4 5 6 7
## 8.958e+00 8.062e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 9.219e+00
## 8 9 10 11 12 13 15
## 2.500e+00 1.333e+01 2.318e+01 1.183e+01 1.524e+01 1.283e+01 1.698e+01
## 16 17 18 19 20 21 22
## 0.000e+00 0.000e+00 9.501e+00 0.000e+00 0.000e+00 8.958e+00 1.167e+01
## 23 24 25 26 27 28 29
## 0.000e+00 0.000e+00 1.455e+01 6.633e+00 1.169e+01 1.309e+01 1.251e+01
## 30 31 32 33 34 35 36
## 9.605e+00 8.958e+00 1.572e+01 1.450e+01 1.183e+01 1.239e+01 0.000e+00
## 37 38 39 40 41 42 43
## 1.608e+01 4.796e+00 1.477e+01 1.223e+01 1.336e+01 1.024e+01 1.119e+01
## 44 45 46 47 48 49 50
## 6.110e+00 0.000e+00 0.000e+00 0.000e+00 1.327e+01 0.000e+00 0.000e+00
## 51 52 53 54 55 56 57
## 0.000e+00 1.141e-13 0.000e+00 0.000e+00 1.419e+01 1.724e+01 1.109e+01
## 58 60 61 62 63 64 65
## 0.000e+00 1.502e+01 0.000e+00 0.000e+00 1.246e+01 0.000e+00 1.327e+01
## 66 67 68 69 70 71 72
## 1.077e+01 4.372e+00 1.374e+01 1.389e+01 1.042e+01 8.007e+00 1.693e+01
## 73 74 75 76 77 78 79
## 1.582e+01 6.620e+00 5.972e+00 7.681e+00 1.201e+01 8.419e+00 1.873e-14
## 80 81 82 83 84 85 86
## 1.074e+01 1.083e+01 8.104e-14 1.310e-13 2.981e+00 0.000e+00 1.619e+01
## 87 88 89 90 91 92 93
## 1.327e+01 0.000e+00 0.000e+00 0.000e+00 1.136e+01 1.150e+01 1.242e+01
## 94 95 96 97 98 99 100
## 1.864e+01 1.532e+01 0.000e+00 1.296e+01 0.000e+00 0.000e+00 1.167e+01
## 101 102 103 104 105 106 107
## 0.000e+00 1.031e+01 1.183e+01 0.000e+00 0.000e+00 1.007e+01 9.055e+00
## 108 109 110
## 1.327e+01 0.000e+00 8.958e+00
##
## Eigenvalues for PCoA axes:
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7
## 38212.716 28710.788 7635.598 3541.055 2363.256 1893.255 1344.076
## PCoA8
## 1007.922
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 107 9409.364 87.93799 1.14485 999 0.23
## Residuals 213 16360.917 76.81182 NA NA NA
##
## Call:
## adonis(formula = df ~ bs_vec + subj_vec)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## bs_vec 13 1.0182 0.078325 3.6882 0.12427 0.001 ***
## subj_vec 106 2.9070 0.027424 1.2914 0.35478 0.007 **
## Residuals 201 4.2685 0.021236 0.52095
## Total 320 8.1937 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1run_bs_subj_adonis(dat8,dat4$Body.site,dat4$Subject.Id)##
## Call:
## adonis(formula = df ~ bs_vec)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## bs_vec 13 2.6985 0.207574 13.053 0.35597 0.001 ***
## Residuals 307 4.8822 0.015903 0.64403
## Total 320 7.5806 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist(df), group = bs_vec)
##
## No. of Positive Eigenvalues: 273
## No. of Negative Eigenvalues: 0
##
## Average distance to median:
## anterior nares attached keratinized gingiva
## 59.25 13.46
## buccal mucosa hard palate
## 15.25 0.00
## left retroauricular crease palatine tonsil
## 36.34 18.84
## posterior fornnix right antecubital fossa
## 11.70 0.00
## right retroauricular crease saliva
## 30.77 0.00
## stool subgingival_plaque
## 19.88 0.00
## supragingival plaque tongue dorsum
## 15.45 24.87
##
## Eigenvalues for PCoA axes:
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6
## 887478.184 21886.524 11903.051 6700.142 3869.472 3275.460
## PCoA7 PCoA8
## 2899.322 2207.445
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 13 89135.39 6856.568 5.243688 999 0.026
## Residuals 307 401428.63 1307.585 NA NA NA
##
## Call:
## adonis(formula = df ~ subj_vec)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## subj_vec 107 2.2476 0.021006 0.83897 0.2965 0.849
## Residuals 213 5.3330 0.025038 0.7035
## Total 320 7.5806 1.0000
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist(df), group = subj_vec)
##
## No. of Positive Eigenvalues: 273
## No. of Negative Eigenvalues: 0
##
## Average distance to median:
## 1 2 3 4 5 6 7
## 1.539e+01 2.478e+01 0.000e+00 0.000e+00 0.000e+00 0.000e+00 6.086e+01
## 8 9 10 11 12 13 15
## 8.194e+01 2.807e+01 4.872e+01 2.177e+01 5.367e+01 6.204e+01 2.751e+01
## 16 17 18 19 20 21 22
## 0.000e+00 0.000e+00 4.703e+01 0.000e+00 0.000e+00 1.135e+01 1.012e+01
## 23 24 25 26 27 28 29
## 0.000e+00 0.000e+00 4.029e+01 1.441e+01 3.130e+01 2.754e+01 2.075e+01
## 30 31 32 33 34 35 36
## 2.785e+01 1.873e+02 3.334e+01 1.780e+01 9.517e+01 3.897e+01 0.000e+00
## 37 38 39 40 41 42 43
## 5.415e+01 3.283e+01 4.081e+01 2.562e+01 3.258e+01 5.613e+01 1.326e+02
## 44 45 46 47 48 49 50
## 7.474e+01 0.000e+00 0.000e+00 0.000e+00 8.718e+00 0.000e+00 0.000e+00
## 51 52 53 54 55 56 57
## 0.000e+00 4.538e+01 0.000e+00 0.000e+00 2.777e+01 2.867e+01 2.996e+01
## 58 60 61 62 63 64 65
## 0.000e+00 2.050e+01 0.000e+00 0.000e+00 2.972e+01 0.000e+00 1.621e+01
## 66 67 68 69 70 71 72
## 1.766e+01 3.952e+01 1.271e+01 1.440e+01 1.878e+01 1.801e+01 1.955e+01
## 73 74 75 76 77 78 79
## 2.156e+01 1.389e+01 1.505e+01 2.324e+01 1.605e+01 8.174e+00 7.483e+00
## 80 81 82 83 84 85 86
## 1.517e+01 1.523e+01 1.265e-11 1.119e-11 2.645e+01 0.000e+00 1.301e+01
## 87 88 89 90 91 92 93
## 8.718e+00 0.000e+00 0.000e+00 0.000e+00 1.017e+01 3.750e+01 1.101e+01
## 94 95 96 97 98 99 100
## 2.281e+01 1.600e+01 0.000e+00 6.391e+01 0.000e+00 0.000e+00 1.364e+01
## 101 102 103 104 105 106 107
## 0.000e+00 2.131e+01 1.540e+01 0.000e+00 0.000e+00 2.302e+01 1.028e+01
## 108 109 110
## 8.718e+00 0.000e+00 1.497e+01
##
## Eigenvalues for PCoA axes:
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6
## 887478.184 21886.524 11903.051 6700.142 3869.472 3275.460
## PCoA7 PCoA8
## 2899.322 2207.445
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 107 199975.6 1868.931 1.202623 999 0.263
## Residuals 213 331011.7 1554.045 NA NA NA
##
## Call:
## adonis(formula = df ~ bs_vec + subj_vec)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## bs_vec 13 2.6985 0.207574 13.3268 0.35597 0.001 ***
## subj_vec 106 1.7515 0.016523 1.0608 0.23104 0.336
## Residuals 201 3.1307 0.015576 0.41299
## Total 320 7.5806 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Permutation tests
#test ffor whether intra-subject distance greater than intersubject
subject_perm(dat4,multiSubjects,dat6)
## Score for intraperson hits = 1403
## [1] "Quartlies for random distribution"
## 0% 25% 50% 75% 100%
## 1458 1545 1564 1583 1663
## Empirical p value = 0#now look at the same test between body sites
bs <- levels(dat4$Body.site)
by_factor_perm(bs,dat4,dat6)## [1] "anterior nares"
## [1] "Number of samples " "67"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 4094 4966 5144 5326 6198
## [1] 5144
## Empirical p value [1] 0.5004
##
## [1] "attached keratinized gingiva"
## [1] "Number of samples " "4"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 0 12 14 16 28
## [1] 8
## Empirical p value [1] 0.0477
##
## [1] "buccal mucosa"
## [1] "Number of samples " "54"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 2607.0 3197.0 3327.5 3459.0 4004.0
## [1] 2725
## Empirical p value [1] 7e-04
##
## Zero samples in hard palate[1] "left retroauricular crease"
## [1] "Number of samples " "23"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 316 550 588 626 826
## [1] 644
## Empirical p value [1] 0.8438
##
## [1] "palatine tonsil"
## [1] "Number of samples " "6"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 5 30 35 40 61
## [1] 36
## Empirical p value [1] 0.5851
##
## [1] "posterior fornnix"
## [1] "Number of samples " "9"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 16 74 84 92 138
## [1] 48
## Empirical p value [1] 0.0074
##
## Zero samples in right antecubital fossa[1] "right retroauricular crease"
## [1] "Number of samples " "28"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 535 828 879 929 1189
## [1] 1091
## Empirical p value [1] 0.9979
##
## Zero samples in saliva[1] "stool"
## [1] "Number of samples " "7"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 6 42 48 56 84
## [1] 30
## Empirical p value [1] 0.0314
##
## Zero samples in subgingival_plaque[1] "supragingival plaque"
## [1] "Number of samples " "37"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 1082 1474 1552 1626 1996
## [1] 1164
## Empirical p value [1] 7e-04
##
## [1] "tongue dorsum"
## [1] "Number of samples " "82"
## [1] "Distribution of random hits"
## 0% 25% 50% 75% 100%
## 6440 7499 7732 7965 8977
## [1] 7998
## Empirical p value [1] 0.7792Plots of subtype distribution
presence_mat <- as.data.frame(bintr(dat5,0.2))
top_score_mat <- as.data.frame(bintr(dat5,0.5))
# png("~/Dropbox/ARTICLES_BY_TDR/2015-staph-metagenome/HMP_barchart.png",width=640, height =640, res = 75)
# dev.off()
genotypes_plot(presence_mat,"Top CCs, 0.025X cutff, subtypes present > 0.2")
genotypes_plot(top_score_mat,"Top CCs, 0.025X cutoff, subtypes present > 0.5")
all_genotypes_plot(presence_mat,"All CCs, 0.025X cutoff, subtypes present > 0.2")
all_genotypes_plot(top_score_mat,"All CCs, 0.025X cutoff, subtypes present > 0.5")
for (i in bs) {
bss_rows <- which(dat4$Body.site == i)
if(length(bss_rows) > 0) {
bs_df <- slice(presence_mat,bss_rows)
genotypes_plot(bs_df,paste("Present: ", i))
}
}
for (i in bs) {
bss_rows <- which(dat4$Body.site == i)
if(length(bss_rows) > 0) {
bs_df <- slice(top_score_mat,bss_rows)
genotypes_plot(bs_df,paste("Top score: ", i))
}
}
###PCA
par(mfrow=c(2,2))
pcobj <- prcomp(dat6)
tr_gray <- rgb(0.5,.5,.5,.15)
for (i in bs) {
prcols <- rep(tr_gray,nrow(dat6))
prcols[which(dat4$Body.site == i)] <- "red"
plot(pcobj$x,col = prcols, pch = 16, main = i)
}
for (i in multiSubjects$Subject.Id) {
sub_rows = which(dat4$Subject.Id == as.character(i))
if (length(sub_rows) > 3){
prcols <- rep(tr_gray,nrow(dat6))
prcols[sub_rows] <- "blue"
plot(pcobj$x,col = prcols, pch = 16, main = c("Subject",i))
}
}
###Session Info
sessionInfo()## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] assertthat_0.1 vegan_2.3-0 lattice_0.20-33
## [4] permute_0.8-4 gdata_2.17.0 RColorBrewer_1.1-2
## [7] e1071_1.6-7 dplyr_0.4.2 reshape2_1.4.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.0 cluster_2.0.3 knitr_1.11 magrittr_1.5
## [5] MASS_7.3-44 R6_2.1.1 stringr_1.0.0 plyr_1.8.3
## [9] tools_3.2.1 parallel_3.2.1 grid_3.2.1 nlme_3.1-122
## [13] mgcv_1.8-7 DBI_0.3.1 htmltools_0.2.6 class_7.3-14
## [17] gtools_3.5.0 lazyeval_0.1.10 yaml_2.1.13 digest_0.6.8
## [21] Matrix_1.2-2 evaluate_0.7.2 rmarkdown_0.8 stringi_0.5-5